Articles | Volume 26, issue 8
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Utkarsh Mital
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Ethan T. Coon
Climate Change Science Institute & Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Dipankar Dwivedi
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., and
Stoffelen, A.: Characterization of precipitation product errors across the
United States using multiplicative triple collocation, Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, 2015. a
Aquanty, I.: HydroGeoSphere User Manual, Waterloo, Ontario, https://www.aquanty.com/hgs-download (last access: 28 April 2022), 2015. a
Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, W. E., and
Radeloff, V. C.: Evaluation of downscaled, gridded climate data for the
conterminous United States, Ecol. Appl., 26, 1338–1351, https://doi.org/10.1002/15-1061, 2016. a, b, c, d
Bolton, D.: The Computation of Equivalent Potential Temperature, Mon. Weather Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Bruni, G., Reinoso, R., Van De Giesen, N. C., Clemens, F. H., and Ten Veldhuis, J. A.: On the sensitivity of urban hydrodynamic modelling to
rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015. a
Coon, E. T. and Shuai, P.: Watershed Workflow, [Computer Software],
https://doi.org/10.11578/dc.20211008.1, 2021. a
Coon, E. T., Svyatskiy, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A. L., Harp, D. R., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, J. D., Karra, S., Painter, S. L., Jafarov, E., and Molins, S.:
Advanced Terrestrial Simulator (ATS), US DOE Office of Science (SC),
Biological and Environmental Research (BER), https://doi.org/10.11578/dc.20190911.1,
2019. a, b, c
Coon, E. T., Moulton, J. D., Kikinzon, E., Berndt, M., Manzini, G., Garimella, R., Lipnikov, K., and Painter, S. L.: Coupling surface flow and subsurface flow in complex soil structures using mimetic finite differences, Adv. Water Resour., 144, 103701, https://doi.org/10.1016/j.advwatres.2020.103701, 2020. a
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and
retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002jd003118, 2003. a, b
Cromwell, E., Shuai, P., Jiang, P., Coon, E. T., Painter, S. L., Moulton, J. D., Lin, Y., and Chen, X.: Estimating Watershed Subsurface Permeability
From Stream Discharge Data Using Deep Neural Networks, Front. Earth Sci., 9, 1–13, https://doi.org/10.3389/feart.2021.613011, 2021. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b, c, d, e, f, g
Elsner, M. M., Gangopadhyay, S., Pruitt, T., Brekke, L. D., Mizukami, N., and
Clark, M. P.: How Does the Choice of Distributed Meteorological Data Affect
Hydrologic Model Calibration and Streamflow Simulations?, J. Hydrometeorol., 15, 1384–1403, https://doi.org/10.1175/jhm-d-13-083.1, 2014. a, b
Eum, H. I., Dibike, Y., Prowse, T., and Bonsal, B.: Inter-comparison of
high-resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada, Hydrol. Process., 28, 4250–4271, https://doi.org/10.1002/hyp.10236, 2014. a
Ficchì, A., Perrin, C., and Andréassian, V.: Impact of temporal
resolution of inputs on hydrological model performance: An analysis based on
2400 flood events, J. Hydrol., 538, 454–470, https://doi.org/10.1016/j.jhydrol.2016.04.016, 2016. a
Gao, J., Sheshukov, A. Y., Yen, H., and White, M. J.: Impacts of alternative
climate information on hydrologic processes with SWAT: A comparison of NCDC,
PRISM and NEXRAD datasets, Catena, 156, 353–364, https://doi.org/10.1016/j.catena.2017.04.010, 2017. a
Gatzke, S. E., Beaudette, D. E., Ficklin, D. L., Luo, Y., O'Geen, A. T., and
Zhang, M.: Aggregation Strategies for SSURGO Data: Effects on SWAT Soil
Inputs and Hydrologic Outputs, Soil Sci. Soc. Am. J., 75, 1908–1921, https://doi.org/10.2136/sssaj2010.0418, 2011. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and
Mapping Global Permeability of the Unconsolidated and Consolidated Earth:
GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Ko, A., Mascaro, G., and Vivoni, E. R.: Strategies to Improve and Evaluate
Physics-Based Hyperresolution Hydrologic Simulations at Regional Basin
Scales, Water Resour. Res., 55, 1129–1152, https://doi.org/10.1029/2018WR023521, 2019. a, b
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow
modeling: A free-surface overland flow boundary condition in a parallel
groundwater flow model, Adv. Water Resour., 29, 945–958,
https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Koppen, W. and Geiger, R.: Handbook of climatology, vol. 1, Gebruder
Borntraeger, Berlin, 1930. a
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on
leveraging synergy in multiple meteorological data sets with deep learning
for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, 2021. a
Loheide, S. P. and Lundquist, J. D.: Snowmelt-induced diel fluxes through the
hyporheic zone, Water Resour. Rese., 45, W07404, https://doi.org/10.1029/2008WR007329, 2009. a
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of
meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020. a, b, c
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and
transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016. a, b
Mital, U., Dwivedi, D., Brown, J., and Steefel, C.:: Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008–2019; daily temporal resolution; 400 m spatial resolution, ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1822259, 2021. a
Mital, U., Dwivedi, D., Brown, J. B., and Steefel, C. I.: Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for East Taylor subbasin (western United States), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-67, in review, 2022. a
Mitchell, K. E.: The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90, https://doi.org/10.1029/2003JD003823, 2004. a
Mourtzinis, S., Rattalino Edreira, J. I., Conley, S. P., and Grassini, P.: From grid to field: Assessing quality of gridded weather data for agricultural applications, Eur. J. Agron., 82, 163–172, https://doi.org/10.1016/j.eja.2016.10.013, 2017. a
Muche, M. E., Sinnathamby, S., Parmar, R., Knightes, C. D., Johnston, J. M.,
Wolfe, K., Purucker, S. T., Cyterski, M. J., and Smith, D.: Comparison and
Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural
Watershed Using SWAT, J. Am. Water Resour. Assoc., 56, 486–506, https://doi.org/10.1111/1752-1688.12819, 2020. a, b, c, d, e
Ochoa-Rodriguez, S., Wang, L. P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., Van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I.,
Onof, C., Willems, P., and Ten Veldhuis, M. C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015. a, b, c
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z. M., Swenson, S., Thornton, P. E.,
Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S. P., Ricciuto, D. M.,
Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), No. NCAR/TN-503+STR, NCAR, p. D6RR1W7M, https://doi.org/10.5065/D6RR1W7M, 2013. a
Pan, M., Sheffield, J., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., and Tarpley, J. D.: Snow process modeling in the North
American Land Data Assimilation System (NLDAS): 1. Evaluation of model simulated snow water equivalent, J. Geophys. Res.-Atmos., 108, 1–14, https://doi.org/10.1029/2003jd003994, 2003. a
Pan, M., Cai, X., Chaney, N. W., Entekhabi, D., and Wood, E. F.: An initial
assessment of SMAP soil moisture retrievals using high-resolution model
simulations and in situ observations, Geophys. Res. Lett., 43, 9662–9668, https://doi.org/10.1002/2016GL069964, 2016. a
Petrone, K., Buffam, I., and Laudon, H.: Hydrologic and biotic control of
nitrogen export during snowmelt: A combined conservative and reactive tracer
approach, Water Resour. Res., 43, 1–13, https://doi.org/10.1029/2006WR005286, 2007. a
Schreiner‐McGraw, A. P. and Ajami, H.: Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain, Water Resour. Res., 56, 1–21, https://doi.org/10.1029/2020WR027639, 2020. a, b
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017. a
Sheffield, J., Pan, M., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Ramsay, B. H.: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent, J. Geophys. Res.-Atmos., 108, 1–13, https://doi.org/10.1029/2002jd003274, 2003. a
Shuai, P., Cardenas, M. B., Knappett, P. S. K., Bennett, P. C., and Neilson,
B. T.: Denitrification in the banks of fluctuating rivers: The effects of river stage amplitude, sediment hydraulic conductivity and dispersivity, and
ambient groundwater flow, Water Resour. Res., 53, 7951–7967, https://doi.org/10.1002/2017WR020610, 2017. a
Shuai, P., Chen, X., Mital, U., Coon, E., and Dwivedi, D.: Data-model files associated with the manuscript “The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses” (Shuai et al., 2022 HESS), ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1861432, 2022. a
Song, X., Chen, X., Stegen, J., Hammond, G., Song, H.-S., Dai, H., Graham, E., and Zachara, J. M.: Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone, Water Resour. Res., 54, 7361–7382,
https://doi.org/10.1029/2018WR022586, 2018. a
Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., and
Hunkeler, D.: Your work is my boundary condition!: Challenges and approaches
for a closer collaboration between hydrologists and hydrogeologists, J. Hydrol., 571, 235–243, https://doi.org/10.1016/j.jhydrol.2019.01.058, 2019. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of
daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a, b, c, d
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and Wilson,
B. E.: Gridded daily weather data for North America with comprehensive
uncertainty quantification, Scient. Data, 8, 1–17, https://doi.org/10.1038/s41597-021-00973-0, 2021. a, b, c, d
Wetterhall, F., He, Y., Cloke, H., and Pappenberger, F.: Effects of temporal
resolution of input precipitation on the performance of hydrological forecasting, Adv. Geosci., 29, 21–25, https://doi.org/10.5194/adgeo-29-21-2011, 2011. a
Woelber, B., Maneta, M. P., Harper, J., Jencso, K. G., Payton Gardner, W.,
Wilcox, A. C., and López-Moreno, I.: The influence of diurnal snowmelt and transpiration on hillslope throughflow and stream response, Hydrol. Earth Syst. Sci., 22, 4295–4310, https://doi.org/10.5194/hess-22-4295-2018, 2018.
a
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.: Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res.-Atmos., 117, D03110, https://doi.org/10.1029/2011JD016051, 2012. a, b
Zhang, Y. and Schaap, M. G.: Weighted Recalibration of the Rosetta Pedotransfer Model with Improved Estimates of Hydraulic Parameter Distributions and Summary Statistics (Rosetta3), J. Hydrol., 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017. a
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven...